{"url":"/dataset/smlm-cep152-complex-fits-images","name":"SMLM CEP152-Complex FITS Images","full_name":"SMLM CEP152-Complex FITS Images","description_markdown":"The following files comprise 19 sets of 40,000 images, each set corresponding to a different rendering sigma as described in the paper.\r\n\r\n* Extracting *\r\n\r\nTo extract the files, execute the following command (under Linux):\r\n\r\ncat paper_data.tar.gz.* | tar xzvf -\r\n\r\n \r\n\r\n* Organisation *\r\n\r\nThe data are grouped into 19 directories, corresponding to the sigma value they were rendered at. These values are\r\n\r\n10, 9, 8.1,  7.29, 6.56, 5.9, 5.31, 4.78, 4.3, 3.87, 3.65, 3.28, 2.95, 2.66, 2.39, 2.15, 1.94, 1.743, 1.57, 1.41\r\n\r\nEach directory contains 40,000 FITS files - a NASA floating point image standard. The images are single channel and un-normalised. This structure is ready to be used\r\n\r\n* Recreating the data *\r\n\r\nIf you have the time and compute power, you can regenerate this data set with as many or as few images as you prefer, at any sigma level. The original experimental data is available at <fill in later>.\r\n\r\nTo recreate the data set you need to download the CEPRender program, available on github: https://github.com/OniDaito/CEPrender - details on how to use this program are available with the code.","description_withheld":null,"homepage":"https://zenodo.org/record/4751057","introduced_date":"2021-10-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/3d-structure-from-2d-microscopy-images-using","title":"3D Structure from 2D Microscopy images using Deep Learning","first_author":"Benjamin J. Blundell","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SMLM CEP152-Complex FITS Images"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}